Paper No. 18
Presentation Time: 1:30 PM


MUKHERJEE, Arindam, University, MS 38677 and ZACHOS, Louis G., Geology and Geological Engineering, University of Mississippi, 118G Carrier Hall, Oxford, MS 38677,

Sinkholes can be considered as a subset of depression contours. This study automates the identification of depression contours using a methodology based on the currently available set of tools in ArcGIS and predicts sinkhole occurrences based on prior/additional knowledge of the study area in Nixa, southwest Missouri.

The tool takes a Digital Elevation Model (DEM) as primary input data and uses DEM subtraction as the primary method to delineate depression contours. The two DEMs used in this study had a resolution of 10m (source: MSDIS) and 6 inches (derived from LiDAR, source: USGS). First, a contour file is generated from the input DEM and then the local minima of the DEM are filled. A second set of contours are generated from the filled DEM. Based on these two contour sets and locations, the group of closed contours that are completely within the filled area are selected as depression contours. Spatial features such as perimeter, area, maximum depth and volume help to detect true sinkholes based on site specific criteria. In this case, the prior knowledge of the average sinkhole area in the study area (already in the database) was chosen to select the group of sinkholes. After the depression contours were selected, they were converted into polygons and their area calculated. Then the sinkholes were extracted based on the threshold of known average area of the existing sinkholes.

The model/tool successfully predicted the sinkholes that are already in the database of state of Missouri (MODNR) for the area (total of 217) using both high and lower resolution DEM and contour interval. In addition to that, the model delineated several closed depressions which could be the future locations of sinkholes.